Classification of media in a media sharing system

ABSTRACT

Systems and methods for classifying media items in a media system are provided. In particular, media items can be uploaded to a serve. Data describing the media items can be monitored. Alterations of data describing the media items or inconsistencies of the data describing can be detected. A corrective action can be determined based on the alterations and or the inconsistencies. The corrective action can manage media items in multiple classification systems.

TECHNICAL FIELD

This disclosure generally relates to classifying media content in amedia sharing system and/or altering data describing media content in amedia hosting system.

BACKGROUND

The proliferation of available media items is increasing at exponentiallevels that will soon reach many millions if not billions of suchviewable media content. With the ubiquitous nature of media creation andpublishing tools, individuals are able to become productive contentcreators. This has resulted in exceptional growth of available mediaitems.

Media items can be classified according to composition, subject area,age groups, and the like. With the growth of available media items, itis inevitable that related media items or portions of media items areuploaded to websites. Conventionally, each media item is assigned itsown descriptive data. Manual analysis of media content is highlyinefficient considering the large body of available media content.

SUMMARY

The following presents a simplified summary of the specification inorder to provide a basic understanding of some aspects of thespecification. This summary is not an extensive overview of thespecification. It is intended to neither identify key or criticalelements of the specification nor delineate the scope of any particularembodiments of the specification, or any scope of the claims. Itspurpose is to present some concepts of the specification in a simplifiedform as a prelude to the more detailed description that is presented inthis disclosure.

Systems disclosed herein relate to classifying media items in a massivemedia item system. A detection component can detect change eventsassociated with media items. The change events can represent a change inclassification, cluster, or the like. In another aspect, the changeevent can represent an inconsistency of descriptive data. A policycomponent can determine a corrective action based on the change event.The corrective action can comprise altering descriptive data and/orgenerating a report describing the change event. A merging component canmerge and/or alter descriptive data of media items based on thecorrective action.

Other embodiments relate to methods for altering descriptive data and/orclassifying media items in a massive media item system. For example, aserver that distributes user-broadcasted media content. Inconsistenciesand changes associated with descriptions of media items relating to acommon cluster can be corrected based on alteration policies.

The following description and the drawings set forth certainillustrative aspects of the specification. These aspects are indicative,however, of but a few of the various ways in which the principles of thespecification may be employed. Other advantages and novel features ofthe specification will become apparent from the following detaileddescription of the specification when considered in conjunction with thedrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Numerous aspects, embodiments, objects and advantages of the presentinvention will be apparent upon consideration of the following detaileddescription, taken in conjunction with the accompanying drawings, inwhich like reference characters refer to like parts throughout, and inwhich:

FIG. 1 illustrates a high-level block diagram of an example system thatcan alter descriptive data of media items and classify media items inaccordance with certain embodiments of this disclosure;

FIG. 2 illustrates a diagram of media items that can be classified inaccordance with certain embodiments of this disclosure;

FIG. 3 illustrates a high-level block diagram of an example system thatcan detect changes in descriptive data and can alter descriptive data ofmedia items and manage classification databases in accordance withcertain embodiments of this disclosure;

FIG. 4 illustrates a high-level block diagram of an example system thatcan detect inconsistencies in classification systems including areporting component, in accordance with certain embodiments of thisdisclosure;

FIG. 5 illustrates a high-level block diagram of an example system thatcan detect inconsistencies in classification systems including anediting component, in accordance with certain embodiments of thisdisclosure;

FIG. 6 illustrates an example methodology that can detect change eventsand can correct inconsistencies in accordance with certain embodimentsof this disclosure;

FIG. 7 illustrates an example methodology that can detect change eventsand can generate a corrective action in accordance with certainembodiments of this disclosure;

FIG. 8 illustrates an example methodology that can detect change eventsand can select data for alterations in accordance with certainembodiments of this disclosure;

FIG. 9 illustrates an example schematic block diagram for a computingenvironment in accordance with certain embodiments of this disclosure;and

FIG. 10 illustrates an example block diagram of a computer operable toexecute certain embodiments of this disclosure.

DETAILED DESCRIPTION

Various aspects or features of this disclosure are described withreference to the drawings, wherein like reference numerals are used torefer to like elements throughout. In this specification, numerousspecific details are set forth in order to provide a thoroughunderstanding of this disclosure. It should be understood, however, thatcertain aspects of disclosure may be practiced without these specificdetails, or with other methods, components, materials, etc. In otherinstances, well-known structures and devices are shown in block diagramform to facilitate describing this disclosure.

In situations in which the systems discussed herein collect personalinformation about users, or may make use of personal information, theusers may be provided with an opportunity to control whether programs orfeatures collect user information (e.g., information about a user'ssocial network, social actions or activities, profession, a user'spreferences, or a user's current location), or to control whether and/orhow to receive content from the content server that may be more relevantto the user. In addition, certain data may be treated in one or moreways before it is stored or used, so that personally identifiableinformation is removed. For example, a user's identity may be treated sothat no personally identifiable information can be determined for theuser, or a user's geographic location may be generalized where locationinformation is obtained (such as to a city, ZIP code, or state level),so that a particular location of a user cannot be determined. Thus, theuser may have control over how information is collected about the userand used by a content server. Moreover, one or more implementationsdescribed herein can provide for anonymizing collected, received, ortransmitted data.

In accordance with one or more implementations described in thisdisclosure, a system can classify media items and/or merge descriptivedata associated with media items. Media items can comprise video, audio,text, and/or a combination of the above. A media item classificationsystem can alter descriptors of identified media items, reduce cost ofsearch results, control copying of media items, increase reliability ofdescriptive data, and provide for a more robust media sharing system. Ina media item service, classification of media items can reduce cost andincrease overall efficiency of a system.

A detection component detect changes of data describing a media item.The data describing the media item (“descriptive data”) can include metadata, classification data, cluster data, data describing an aspect orfeature of a media item, and the like. A change can occur based on userinput, automated editing of descriptive data, uploading of a new mediaitem, matching a media item to another media item, and the like. Acluster can represent a set of classifiers that describe a media itemand/or set of media items. In an example, a user can alter a descriptionof a media item to rate the media item as pertaining to a “mature”audience and the detection component can detect the alteration.

A policy component can, in response to the detection component detectingthe change, determine a matching media item associated with the mediaitem and/or determine a policy for altering descriptive data of matchedmedia items and/or associated media items. For example, a policycomponent can determine that a first media item, associated with achange, is associated with a second media item. The data describing afirst media item of a video clip can be altered and the first media itemcan be identified, by the policy component, as associated (e.g., aduplicate and/or partial duplicate, sharing common features, etc.) witha second media item that contains the video clip. In another aspect thepolicy component can determine an action based on the altereddescriptive data.

A merging component can determine whether to alter, in response to thepolicy component determining the matching media item, the descriptivedata of related media items. In an aspect, the merging component canalter the descriptive data based on an action determined according to amerging policy. For example, the merging component can determine toalter descriptive data of a first media item if descriptive data of asecond related media is altered. In another example, the mergingcomponent can determine to not alter descriptive data of a first mediaitem if exclusive descriptive data of a second related media item isaltered. Various other aspects are described in more detail herein.

In implementations, the components described herein can perform actions,in real-time, near real-time, online and/or offline. Online/offline canrefer to states identifying connectivity between one or more components.In general, “online” indicates a state of connectivity, while “offline”indicates a disconnected state. In an aspect, offline merging canprevent service interruptions, end-user quality degradation, and thelike.

While the various components are illustrated as separate components, itis noted that the various components can be comprised of one or moreother components. Further, it is noted that the embodiments can compriseadditional components not shown for sake of brevity. Additionally,various aspects described herein may be performed by one device or twoor more devices in communication with each other.

Referring now to FIG. 1, a system 100 is depicted. System 100 canclassify media items and alter descriptive data of related media items.Embodiments disclosed herein, for example, can detect a change event,such as a change and/or inconsistency in descriptive data of media itemsin real-time and/or near real time and alter descriptive data of relatedmedia items, such as clusters, classifiers, meta-data, and the like.Such can enable additional features and improve user satisfaction, andcan be particularly useful in massive media systems. System 100 caninclude a memory 104 that stores computer executable components and aprocessor 102 that executes computer executable components stored in thememory 104. It is to be appreciated that the system 100 can be used inconnection with implementing one or more of the systems or componentsshown and described in connection with other figures disclosed herein.It is noted that all or some aspects of system 100 can be comprised inlarger systems such as servers, computing devices, smart phones, and thelike. As depicted, system 100 can include a detection component 110(which can detect changes to descriptive data), a policy component 120(which can detect related media items and determine corrective actionsbased on a policy), and a merging component 130 (which can alterdescriptive data of media items).

System 100 can receive input 106 as a media item, input requesting achange to descriptive data of a media item, input instructing a changein descriptive data, input notifying of a change in descriptive data,and the like. The detection component 110 can determine if descriptivedata has changed based on the input. For example, a user can alter adescription of a video hosted on a video sharing site. In anotherexample, a system can analyze a video and determine the video containsmature content (e.g., language, violence, etc.). If the descriptive datadoes not indicate the video is for a mature audience, the system canalter the descriptive data of the video and the detection component 110can detect the alteration. In another example, a user can upload a mediaitem and the detection component 110 can detect the cluster,classification, and/or other descriptive data. It is noted that a changein descriptive data can include a newly uploaded media item and/or newlyadded descriptive data.

In implementations, the detection component 110 can detectinconsistencies of descriptive data and/or clusters. Inconsistencies caninclude contradicting classifications, anomalies in descriptive data,and the like. In an aspect, the inconsistencies can be eliminated as thesystem 100 manages the classification data and clusters of various mediaitems. In another aspect, system 100 can utilize a batch algorithm thatprocesses inconsistencies and/or changes at a given time and/orinterval. For example, as media items are uploaded and/or edited byusers, system 100 can store the media items and edits. System 100 canprocess the uploaded media items and/or edits at a given time, such aswhen a website is experiencing the least amount of use and/or when aserver is offline.

Turing to FIG. 2, with reference to FIG. 1, there depicted is anillustrative example system 200 of a media items that can be classifiedby system 100 and/or other systems and methods described herein. As anexample, the detection component 110 can determine a cluster associatedwith media item 206. A cluster can comprise a set of media items (and/orrepresentations of media items) that have a common and/or related set ofdescriptors describing an aspect of a media item. It is noted that mediaitems can be associated with one or more clusters. For example, a mediaitem can be associated with a first cluster associated with an audiochannel, a second cluster associated with a video channel, and a thirdcluster associated with both audio and video channels. It is furthernoted that media items belonging to one cluster can be identical, nearidentical, and/or disparate in such a way that they do not share anyother clusters. As an example, media item 206 of a soccer match canbelong to a first cluster for audio, a second cluster for video, and athird cluster for both audio and video. Media item 208 of the sameand/or similar soccer match can be identical on the video channel butcan be set to music. Thus, media item 208 can belong to the same videocluster as the media item 206, but can belong to different clusters forits audio channel and combination (both audio and video) channel.Continuing with the example, media item 210 may contain similar and/oridentical music as media item 208. Thus, media item 210 can belong tothe same audio cluster as media item 208 but may not belong to any videoor combination cluster. It is noted that media item 210 can belong tovideo or combination clusters associated with media items having noand/or blank video. While clusters are described as along audio, video,and/or both channels, it is noted that a cluster can be associated withother channels as well, such as luminance channels, motion channels,pitch channels, frequency channels, and the like. Further, system 100can utilize various channels and/or clusters.

In implementations, the detection component 110 can determine changesand/or types of changes associated with descriptive data. In an aspect,the types of changes can include cluster changes, classificationchanges, and the like. It is noted that the detection component 110 candetermine whether the type of change and whether a particular change isexclusive or inclusive. As used herein, exclusive descriptive datacomprise data that can change independent of a particular media item,cluster, and/or a particular channel (e.g., audio, video, etc.).Inclusive descriptive data can comprise data that can affect descriptivedata of other media items, clusters, and/or particular channels (e.g.,audio, video, etc.). In another aspect, descriptive data can beinclusive along a certain channel and exclusive along other channels. Itis noted that the detection component 110 can detect changes acrossmultiple classification systems. For example, the detection component110 can detect changes in loosely related classification systems (e.g.,cluster classification and meta-data classifications), tightly coupledclassification systems, and the like.

For example, media items 206, 208, and 210 can have respectivedescriptions. The descriptions can comprise meta-data, tags, and otherdata that describes an aspect of the content. Media item 206 can have arating indicating it is appropriate for all ages and media item 208 canhave rating indicating it is not appropriate for all ages if the musichas profane language. If media item 208 has associated descriptive datadescribing the media items as “rock music” then media item 210 may bedescribed as associated with “rock music”.

The policy component 120 can determine matching media items anddetermine whether to alter descriptive data of matching media items. Inan aspect, a matching media item can comprise a media item that shares acluster with a media item associated with a change event. For example,if descriptive data of media item 206 changes, the policy component 120can determine that media item 208 matches based on a cluster associatedwith both media item 206 and media item 208. In another aspect, thepolicy component 120 can determine a matching media item based oncomparison of media items and/or representations of media items. Forexample, the policy component 120 can analyze media item 206 and/or arepresentation of media item 206 such as a set of compact descriptorsassociated with media item 206. A compact descriptor can represent afingerprint and/or set of sub-fingerprints of media item 206, a digestof media item 206, an index of media item 206, and the like. Policycomponent 120 can determine media items that match media item 206 basedon an analysis of media items. For example, the policy component 120 cananalyze a set of media items and determine media item 208 matches mediaitem 206.

In implementations, the policy component 120 can determine matched mediaitems based a determined channel of media items. In an aspect, thepolicy component 120 can select a channel for matching based on analysisof a change and/or an alteration policy. The analysis of the change caninclude determining a type of change, a field associated with a change,and the like. The alteration policy can include instructions thatindicate preferred and/or determined actions to make in response toanalyzing the change. For example, the policy component 120 candetermine whether to alter descriptive data of media item 208 inresponse to the detection component 110 detecting a change todescriptive data of media item 206. As another example, a user canprovide input to change a description of media item 206 by adding a tagdescribing the data a soccer match occurred (e.g., “Jan. 1, 2000”). Thepolicy component 120 can determine that the tag relates to the videocontent of media item 206 and can identify media item 208 as matchingthe video channel of media item 206. In another example, the policycomponent 120 can receive, from the detection component 110, anindication that a change of a classification of media item 208 occurred.If the classification is changed from “rock music” to “pop music”, thepolicy component can determine the change is associated with an audiochannel of media item 208. The policy component 120 can determinematched media items along the audio channel of media item 208, such asmatching media item 210 (e.g., through an audio cluster). It is notedthat media item 206 is not matched to media item 208 along the audiochannel and the policy component 120 can determine that a change isapplicable to media item 210 but not media item 206.

In another aspect, the policy component 120 can determine that aninconsistency and/or alterations should not be corrected based on analteration policy. For example, a user may desire that their media itemhas a certain classification regardless of classifications of othermedia items. As an example, a user can upload media item 206 anddescribe it as associated with a great performance by a soccer player. Adifferent user associated with media item 208 can describe media item208 as containing a poor performance of the same soccer player. In thisexample, either user can flag their description to indicate that theirdescription should not be altered.

In another example, the policy component 120 can resolve discrepanciesdetected by the detection component 110 based on a policy. As anexample, the detection component can determine that media item 206belongs to a video cluster that is associated with multiple videos. Aclassification of the videos belonging to the cluster can havediscrepancies, for example, media item 206 can be classified as “soccer”while other media items belonging to the cluster can be classified as“rugby”. The policy component 120 can determine a confidence scoreassociated with the classifications and select the classifications withthe highest confidence score. For example, the policy component 120 candetermined that the classification of “soccer” has a higher confidencelevel than the classification of “rugby” and can select “soccer” as theclassification of media items in the cluster. In another aspect, thepolicy component 120 can determine that both classifications should beapplied to both media items. For example, if the confidence scoresassociated with media item 206 and media item 208 are equal, the policycomponent 120 can determine to alter descriptive data of both media item206 and media item 208.

In implementations, a confidence score can represent a probability thatdetermined descriptive data is correct. It is noted that the computationof a confidence score can depend on the descriptive data being scored.In an aspect, a probability that the correct based on a confidencealgorithm and/or based on analysis of media items, user input, analysisof other descriptive data fields, and the like. It is noted that a usercan provide input to override a determined confidence score.

The merging component 130 can determine whether to change descriptivedata of media items. In an aspect, the merging component 130 can receivean instruction from the policy component 120 that indicates descriptivedata should be altered in response to the detection component 110detecting a change. As an example, the merging component 130 candetermine whether descriptive data should be changed based on aconfidence score, user input, and the like. In implementations, themerging component 130 can alter descriptive data associated with one ormore media items. As an example, the merging component 130 can determinethat descriptive data of media item 208 should be changed based on achange to descriptive data of media item 206. The merging component 130can change the descriptive data of media item 208 and/or other mediaitems associated with media item 206. It is noted that the descriptivedata can include a change in classification, cluster, and the like.

Turning now to FIG. 3, a system 300 is depicted. System 300 can detectchanges in descriptive data and can alter descriptive data of mediaitems and manage classification databases. Embodiments disclosed herein,for example, can detect related media items (and/or channels of mediaitems) in real-time, near real time, and/or in batch processing andalter descriptive data such as clusters, classifiers, meta-data, and thelike. System 300 can include a memory that stores computer executablecomponents and a processor that executes computer executable componentsstored in the memory. System 300 can also include a classificationdatabase 322 (which stores classification data of media items) and acluster database 326 (which can store clusters). While system 300 isdescribed as including classification database 322 and cluster database326, it is noted that system 300 can include other databases,alternative classification databases, and/or a single database. It isfurther noted, while classification database 322 and cluster database326 are depicted as separate entities, that classification database 322and cluster database 326 can be a single entity.

As depicted, system 300 can include communicably coupled componentsincluding a detection component 310 (which can detect changes and/ordiscrepancies of descriptive data), a policy component 320 (which canidentify related media items), a merging component 330 (which can alterdescriptive data of media items), and a learning component 340 (whichcan determine alteration policies). It is noted that digest component310, indexing component 320, and lookup component 330 can respectivelyfunctional similarly or to detection component 110, policy component120, and merging component 130. It is to be appreciated that the system300 can include various components described with reference to othersystems described herein (e.g., system 100, system 200, etc.).

In implementations, the classification database 322 can compriseclassification fields related to media items. It is noted that theclassification database 322 can comprise media items and/or referencesto media items that are stored in a disparate database (such as a mediaitem database). It is noted that media items can have any number ofassociated classifications (e.g., 0, 1, 2, . . . , N). Theclassifications can be utilized to describe an aspect of the media itemsuch as upload time, creator, publishing date, subject area, qualityindicators, and the like.

The cluster database 326 can comprise clusters associated with mediaitems. It is noted that the clusters can comprise a cluster identifier(“cluster ID”), a set of media items belong to a cluster, references tomedia items belonging to clusters, and the like. It is further notedthat a media item can be associated with one or more clusters, such asan audio cluster, a video cluster, and/or a combination cluster. In anaspect, a media item can be associated with one cluster per channeland/or multiple clusters per channel.

The detection component 310 can detect changes in the classificationdatabase 322, changes in the cluster database 326, and/or discrepanciesin the classification database 322 and/or the cluster database 326. Asan example, a cluster “L” can be associated with media items (e.g., on achannel) V_(i), V_(j), and V_(k), that is L={V_(i), V_(j), V_(k)}. Eachvideo of L can be associated with classification data represented as c.For example, V_(j) can be associated with c_(x) and c_(y), V_(k) can beassociated with classification c_(z), and V_(i) can be associated withno classifications. Classifications c_(x), c_(y), and c_(z) canrepresent any classification field.

In response to the detection component 310 detecting a classificationchange associated with a media item, such as V_(i), the clusterassociated with the media item can be evaluated. Continuing with theabove example, if a classification of V_(i) is detected, then othermedia items of cluster L (V_(j), and V_(k)) can be evaluated forconsistency. It is noted that certain classifications can be independentof certain match types: for example, if c_(z) says that V_(j) containsmusic by Mozart, a match on the video channel will be irrelevant to thisclassification. Similarly, a classification of a video as not suitablefor children due to harsh language is also independent of the videochannel: moreover, harsh language in the metadata is independent of bothaudio and video. A classification saying that a video contains, e.g.,tennis could be regarded as independent of the audio channel, as audioonly tennis can be determined to be not tennis, and video only tenniscan be determined to be tennis.

In implementations, the policy component 320 can determine media itemsrelated to the change event. For example, assume that cx, cy, and cz allare dependent on a video channel, and the change event was with regardsto the video channel. The policy component 320 can determine that anychange associated with a video channels may create an inconsistency withregards to all videos in cluster L. In response to identifying aninconsistency, the policy component 320 can determine a responsiveaction, such as report the inconsistency, alter descriptive data tocorrect the inconsistency, and the like. The policy component 320 hasthe benefit of having access to all media items in a cluster and theirclassifications, so it can make decisions at a higher level than whatwould be possible from a mere inconsistency between two media items.

In implementations, the policy component 320 can determine an actionbased on the classification associated with a change event. For example,for an exclusive classification the policy component can determine toapply the majority classification to all media items in a cluster, applya classification having the highest confidence score, apply themost/least recently altered classification, and the like. It is notedthat the policy component 320 can request manual input and select aclassification based on received input. For example, in a cluster offour videos, where two videos are classifies as Rugby and two asAmerican football, assuming that the confidence in both Rugbyclassifications is higher than the confidence in both American footballclassifications, it is likely that all four videos are Rugby and thepolicy component 320 can select Rugby for all the media items.

The merging component 330 can determine if descriptive data should bechange in the classification database 322 and/or the cluster database326. In an aspect, the merging component 330 can alter classificationsand clusters for one or more media items. In another aspect, the mergingcomponent 330 can determine not to alter descriptive data based oninstructions received from the policy component 320, for example.

The learning component 340 can determine alteration policies and/orconfidence scores based on a history of changes, actions, user input,and the like. The alteration policies can be utilized, for example, bythe policy component 320 when determining an action. In an aspect, thelearning component 340 can store actions taken, types associated withactions, channels associated with actions, classification fieldsassociated with actions, user input, and the like.

The learning component 340 can utilize an artificial intelligence modelthat can facilitate inferring and/or determining when, where, how alterdescriptive data. As used herein, the term “inference” refers generallyto the process of reasoning about or inferring states of the system,environment, and/or user from a set of observations as captured viaevents and/or data. Inference can identify a specific context or action,or can generate a probability distribution over states, for example. Theinference can be probabilistic—that is, the computation of a probabilitydistribution over states of interest based on a consideration of dataand events. Inference can also refer to techniques employed forcomposing higher-level events from a set of events and/or data. Suchinference results in the construction of new events or actions from aset of observed events and/or stored event data, whether or not theevents are correlated in close temporal proximity, and whether theevents and data come from one or several event and data sources.

The learning component 340 can employ any of a variety of suitableartificial intelligence (AI)-based schemes as described supra inconnection with facilitating various aspects of the herein describedinvention. For example, a process for learning explicitly or implicitlyhow parameters are to be created for altering descriptive data accordingto inconsistencies in a channel based on change events can befacilitated via an automatic classification system and process.Inferring and/or learning can employ a probabilistic and/orstatistical-based analysis to infer an action that is to be executed.For example, a support vector machine (SVM) classifier can be employed.Other learning approaches include Bayesian networks, decision trees, andprobabilistic classification models providing different patterns ofindependence can be employed. Learning as used herein also is inclusiveof statistical regression that is utilized to develop models ofpriority.

As will be readily appreciated from the subject specification, thesubject innovation can employ learning classifiers that are explicitlytrained (e.g., via a generic training data) as well as implicitlytrained (e.g., via observing user behavior, receiving extrinsicinformation) so that the learning classifier is used to automaticallydetermine according to a predetermined criteria which action to take.For example, SVM's can be configured via a learning or training phasewithin a learning classifier constructor and feature selection module. Alearning classifier is a function that maps an input attribute vector,x=(x1, x2, x3, x4, xn), to a confidence that the input belongs to alearning class—that is, f(x)=confidence(class).

Referring now to FIG. 4, system 400 is depicted. System 400 can detectinconsistencies in classification systems including reporting detectedinconsistencies. System 400 can include all or portions of systems100-300 as described previously or other systems or components detailedherein. As depicted, system 400 can include communicably coupledcomponents including a detection component 410 (which can detect changesand/or discrepancies of descriptive data), a policy component 420 (whichcan identify related media items), a merging component 430 (which canalter descriptive data of media items), and a reporting component 450(which can report inconsistencies associated with descriptive data ofmedia items).

The reporting component 450 can determine whether to generate a messagethat indicates system 400 is altering descriptive data, is attempting toalter descriptive data, requires user input, and the like. In an aspect,the reporting component 450 can receive an instruction form the policycomponent 420 that indicates whether to report a change and/or attemptedchange. In another aspect, the reporting component 450 can monitorchanges and/or decisions and generate a report based on the changesand/or decisions. In some implementations, the reporting component 450can halt and/or delay a change until occurrence of another event. Forexample, the reporting component 450 can halt a change until input isreceived, until a passage of time, and the like. In an aspect, thereporting component 450 can alert the merging component 430 when achange is no longer halted.

In implementations, the reporting component 450 can generate a messagevia one or more methods. For example, the reporting component 450 cangenerate an email alert, an alert in a computer application (e.g., abrowser), a cellular message, and the like. The message can comprise andata describing a desired action, requesting input, notifying of analterations and the like. It is noted that the reporting component 450can determine recipients of a message based on user input, originaluploaders of media items, users associated with adding classifications,copyright holders of media items, network administrators, and the like.In an aspect, recipients can determined based on a type of alterationsand/or an alteration policy (e.g., received from the policy component420).

Referring now to FIG. 5, system 500 is depicted. System 500 detectsinconsistencies in classification systems including editing data basedon user input. System 500 can include all or portions of systems 100-400as described previously or other systems or components detailed herein.As depicted, system 500 can include communicably coupled componentsincluding a detection component 510 (which can detect changes and/ordiscrepancies of descriptive data), a policy component 520 (which canidentify related media items), a merging component 530 (which can alterdescriptive data of media items), and an editing component 560 (whichcan report inconsistencies associated with descriptive data of mediaitems).

Editing component 560 can receive input 506 in the form of user providedinput, computer generated input, and the like. In an aspect, editingcomponent 560 can receive input 506 to edit descriptive data associatedwith media items and/or edit media items. For example, a user may desirea particular video to be removed from consideration, such as a videoreview of the user's video. In another example, a user can provide inputto edit a media item (e.g., audio, video, and/or both) and the editingcomponent 560 can receive the input and alter the media item.

In implementations, the editing component 560 can receive input to alterdescriptive data of a media item(s), add data describing the mediaitem(s), and determine whether or not the alteration and/or addition isvalid. For example, with reference to FIG. 2, the editing component 560can receive input from a user request to change a classification ofmedia item 206 to “Basketball”. In an aspect, the policy component 520can determine whether or not the change should occur based on aconfidence score of the change and/or the like and the editing component560 can generate an alert (e.g., error message, warning, and the like)to notify a user that the classification is inconsistent, will not beimplement, requires user override, and/or the like.

In another aspect, editing component 560 can receive input identifying aclassification as incorrect and/or inconsistent. For example, mediaitems 206 and 208 can be erroneously classified as “basketball” and auser can provide input indicating that the classification is incorrectand should be “soccer”. It is noted that the user can provide inputindicating one or both of media items 206 and 208 are incorrect. Theediting component 560 can verify the user input (e.g., through aconfidence score, an administrator, and/or the like). In another aspect,the detection component 510 can detect a verified change processed bythe editing component 560.

FIGS. 6-8 illustrate various methodologies in accordance with certainembodiments of this disclosure. While, for purposes of simplicity ofexplanation, the methodologies are shown media a series of acts withinthe context of various flowcharts, it is to be understood andappreciated that embodiments of the disclosure are not limited by theorder of acts, as some acts may occur in different orders and/orconcurrently with other acts from that shown and described herein. Forexample, those skilled in the art will understand and appreciate that amethodology can alternatively be represented as a series of interrelatedstates or events, such as in a state diagram. Moreover, not allillustrated acts may be required to implement a methodology inaccordance with the disclosed subject matter. Additionally, it is to befurther appreciated that the methodologies disclosed hereinafter andthroughout this disclosure are capable of being stored on an article ofmanufacture to facilitate transporting and transferring suchmethodologies to computers. The term article of manufacture, as usedherein, is intended to encompass a computer program accessible from anycomputer-readable device or storage media. It is noted that the methodsdepicted in FIGS. 6-8 can be performed by various systems disclosedherein, such as systems 100, 200, 300, 400 and 500.

FIG. 6 illustrates exemplary method 600. Method 600 can provide foraltering descriptive data of media items and classifying media items ina massive media sharing system. In another aspect, the method 600 candetect change events and can determine correction policies in responseto the change events.

At reference numeral 602, a system can detect (e.g., via detectioncomponent 110) a change event associated with data representing adescription of a media item. The change event can represent an eventthat requires a responsive action. In implementations, the change eventcan be a change in classifications of a media item, changes in a clusterof a media item, an inconsistency in classifications of media items, andthe like. For example, the detection component 110 of FIG. 1 candetermine that classification data of a media items is or has beenaltered. In another example, the detection component 110 can detect aninconsistency between classifications of media items belonging to acommon cluster.

At 604, the system can, in response to detecting the change event,determine (e.g., via policy component 120) a corrective action based onthe change event. The corrective action can comprise alteringdescriptive data, generating a report, and the like. For example, thepolicy component 120 can determine a corrective action based on analteration policy and/or analysis of other media items and associateddescriptive data.

At 606, the system can determine (e.g., via merging component 130)whether to alter, in response determining the corrective action, atleast one of the data representing the description of the media item ordata representing a description of a related media item that isdetermined to be related based on an associated cluster. The relatedmedia item can be related based on a cluster being associated with bothmedia items. In an aspect, the cluster can pertain to a channeldetermined to be associated with the change event. For example, a changeevent can comprise a change to a video classification and the relatedmedia item can be related to a video cluster of the media item. Inanother aspect, the corrective action can be implemented (e.g., viamerging component 130) if it is determined that the change should takeplace. As an example, the merging component 130 can determine that achange is unique to the media item and should not result in alteringdescriptive data of related media items.

Turning now to FIG. 7, exemplary method 700 is depicted. Method 700 canprovide for altering descriptive data of media items and/or generating areport regarding a corrective action. For example, the method 700 canprovide for detecting a type of change event and determining anappropriate corrective action (e.g., altering descriptive data and/orgenerating a report).

At reference numeral 702, a system can detect (e.g., via detectioncomponent 310) a change of the data representing the description of themedia item. The change of data can represent a change in classificationdata, cluster, and the like.

At 704, the system can detect (e.g., via detection component 310) aninconsistency between the data representing the description the mediaitem and the data representing the description of the related mediaitem. For example, the inconsistency can be contradicting classificationdata between two media items having a common cluster. It is noted thatthe cluster can be related to one or more channels.

At 706, the system can determine (e.g., via the policy component 320) atype associated with the at least one of the change or theinconsistency. The type can comprise an inclusive, exclusive, and/orother type. As an example, the policy component 320 can determine ifchanged data or inconsistent data is exclusive or inclusive to othermedia items of a common cluster. In another aspect, the type of changecan comprise a classification change event (e.g., classification data ischanged for one or more media items of a common cluster) or a clusterchange event (e.g., a media item changes an associated cluster).

At 708, the system can determine (e.g., via the policy component 320) acorrective action based on the type of change event and/or an evaluationof the data describing the related media item. For example, thecorrective action can be determined based on an alteration policy. It isnoted that the alteration policy can be predetermined and/or dynamicallydetermined (e.g., learned).

At 710, the system can generate (e.g., via reporting component 450) areport that indicates the determined corrective action. Inimplementations, the report can comprise an error message, a messageindicating a corrective action requires authorization, and the like. Itis noted that the report can be generated as a pop up message, an email,and the like.

At 712, the system can alter (e.g., via merging component 430) at leastone of the data representing the description of the media item or thedata representing a description of the related media item based on thetype associated with the at least one of the change or theinconsistency. For example, a corrective action can indicate whetherdata should be altered to propagate a change and/or correctinconsistencies of classifications. In implementations, a user canprovide input to authorize a change and/or correction. In anotheraspect, the system can generate a report at 710 indicating that a changehas occurred.

Turning now to FIG. 8, example method 800 is illustrated. Method 800 canprovide for altering descriptive data of media items based on analteration policy. For example, the method 800 can provide for selectingdata of associated with a media item for other media items associatedwith a common cluster.

At 802, a system can determine (e.g., via merging component 130) tomerge data describing a first media item and data describing a secondmedia item. For example, a system can determine that a change toclassification of a first media item should be reflected to aclassification of a second media item and/or an inconsistency should becorrected.

At 804, a system can determine (e.g., via merging component 130)confidence levels of the data describing the first media item and thedata describing the second media. In an aspect, the confidence levelscan be determined based on a classification being consistent in amajority of media items of a cluster, user input, analysis ofclassifications of other media items, and the like.

At 806, a system can select (e.g., via merging component 130) the datadescribing the first media item or the data describing the second mediaitem based on respective confidence scores. For example, a system canselect descriptive data of a first media item if the descriptive datahas a higher confidence level than descriptive data of the second mediaitem.

At 808, a system can alter (e.g., via merging component 130) the datadescribing the first media item or the data describing the second mediaitem based on the selected data. For example, a system can alterdescriptive data of a second media item if the descriptive data to matchthe descriptive data of a first media item. It is noted that the systemcan alter a determined data field and/or multiple data fields associatedwith classifications.

The systems and processes described below can be embodied withinhardware, such as a single integrated circuit (IC) chip, multiple ICs,an application specific integrated circuit (ASIC), or the like. Further,the order in which some or all of the process blocks appear in eachprocess should not be deemed limiting. Rather, it should be understoodthat some of the process blocks can be executed in a variety of orders,not all of which may be explicitly illustrated herein.

With reference to FIG. 9, a suitable environment 900 for implementingvarious aspects of the claimed subject matter includes a computer 902.The computer 902 includes a processing unit 904, a system memory 906, acodec 935, and a system bus 908. The system bus 908 couples systemcomponents including, but not limited to, the system memory 906 to theprocessing unit 904. The processing unit 904 can be any of variousavailable processors. Dual microprocessors and other multiprocessorarchitectures also can be employed as the processing unit 904.

The system bus 908 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), Firewire (IEEE 1394), and SmallComputer Systems Interface (SCSI).

The system memory 906 includes volatile memory 910 and non-volatilememory 912. The basic input/output system (BIOS), containing the basicroutines to transfer information between elements within the computer902, such as during start-up, is stored in non-volatile memory 912. Inaddition, according to present innovations, codec 935 may include atleast one of an encoder or decoder, wherein the at least one of anencoder or decoder may consist of hardware, software, or a combinationof hardware and software. For example, in one or more embodiments, allor portions of codec 935 can be included in encoding component 118and/or decoding component 514. Although, codec 935 is depicted as aseparate component, codec 935 may be contained within non-volatilememory 912. By way of illustration, and not limitation, non-volatilememory 912 can include read only memory (ROM), programmable ROM (PROM),electrically programmable ROM (EPROM), electrically erasableprogrammable ROM (EEPROM), or flash memory. Volatile memory 910 includesrandom access memory (RAM), which acts as external cache memory.According to present aspects, the volatile memory may store the writeoperation retry logic (not shown in FIG. 9) and the like. By way ofillustration and not limitation, RAM is available in many forms such asstatic RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), doubledata rate SDRAM (DDR SDRAM), and enhanced SDRAM (ESDRAM.

Computer 902 may also include removable/non-removable,volatile/non-volatile computer storage medium. FIG. 9 illustrates, forexample, disk storage 914. Disk storage 914 includes, but is not limitedto, devices like a magnetic disk drive, solid state disk (SSD) floppydisk drive, tape drive, Jaz drive, Zip drive, LS-100 drive, flash memorycard, or memory stick. In addition, disk storage 914 can include storagemedium separately or in combination with other storage medium including,but not limited to, an optical disk drive such as a compact disk ROMdevice (CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive(CD-RW Drive) or a digital versatile disk ROM drive (DVD-ROM). Tofacilitate connection of the disk storage devices 914 to the system bus908, a removable or non-removable interface is typically used, such asinterface 916. It is appreciated that storage devices 914 can storeinformation related to a user. Such information might be stored at orprovided to a server or to an application running on a user device. Inone embodiment, the user can be notified (e.g., by way of outputdevice(s) 936) of the types of information that are stored to diskstorage 914 and/or transmitted to the server or application. The usercan be provided the opportunity to opt-in or opt-out of having suchinformation collected and/or shared with the server or application(e.g., by way of input from input device(s) 928).

It is to be appreciated that FIG. 9 describes software that acts as anintermediary between users and the basic computer resources described inthe suitable operating environment 900. Such software includes anoperating system 918. Operating system 918, which can be stored on diskstorage 914, acts to control and allocate resources of the computersystem 902. Applications 920 take advantage of the management ofresources by operating system 918 through program modules 924, andprogram data 926, such as the boot/shutdown transaction table and thelike, stored either in system memory 906 or on disk storage 914. It isto be appreciated that the claimed subject matter can be implementedwith various operating systems or combinations of operating systems.

A user enters commands or information into the computer 902 throughinput device(s) 928. Input devices 928 include, but are not limited to,a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 904through the system bus 908 via interface port(s) 930. Interface port(s)930 include, for example, a serial port, a parallel port, a game port,and a universal serial bus (USB). Output device(s) 936 use some of thesame type of ports as input device(s) 928. Thus, for example, a USB portmay be used to provide input to computer 902 and to output informationfrom computer 902 to an output device 936. Output adapter 934 isprovided to illustrate that there are some output devices 936 likemonitors, speakers, and printers, among other output devices 936, whichrequire special adapters. The output adapters 934 include, by way ofillustration and not limitation, video and sound cards that provide ameans of connection between the output device 936 and the system bus908. It should be noted that other devices and/or systems of devicesprovide both input and output capabilities such as remote computer(s)938.

Computer 902 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)938. The remote computer(s) 938 can be a personal computer, a server, arouter, a network PC, a workstation, a microprocessor based appliance, apeer device, a smart phone, a tablet, or other network node, andtypically includes many of the elements described relative to computer902. For purposes of brevity, only a memory storage device 940 isillustrated with remote computer(s) 938. Remote computer(s) 938 islogically connected to computer 902 through a network interface 942 andthen connected via communication connection(s) 944. Network interface942 encompasses wire and/or wireless communication networks such aslocal-area networks (LAN) and wide-area networks (WAN) and cellularnetworks. LAN technologies include Fiber Distributed Data Interface(FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ringand the like. WAN technologies include, but are not limited to,point-to-point links, circuit switching networks like IntegratedServices Digital Networks (ISDN) and variations thereon, packetswitching networks, and Digital Subscriber Lines (DSL).

Communication connection(s) 944 refers to the hardware/software employedto connect the network interface 942 to the bus 908. While communicationconnection 944 is shown for illustrative clarity inside computer 902, itcan also be external to computer 902. The hardware/software necessaryfor connection to the network interface 942 includes, for exemplarypurposes only, internal and external technologies such as, modemsincluding regular telephone grade modems, cable modems and DSL modems,ISDN adapters, and wired and wireless Ethernet cards, hubs, and routers.

Referring now to FIG. 10, there is illustrated a schematic block diagramof a computing environment 1000 in accordance with this specification.The system 1000 includes one or more client(s) 1002 (e.g., laptops,smart phones, PDAs, media players, computers, portable electronicdevices, tablets, and the like). The client(s) 1002 can be hardwareand/or software (e.g., threads, processes, computing devices). Thesystem 1000 also includes one or more server(s) 1004. The server(s) 1004can also be hardware or hardware in combination with software (e.g.,threads, processes, computing devices). The servers 1004 can housethreads to perform transformations by employing aspects of thisdisclosure, for example. One possible communication between a client1002 and a server 1004 can be in the form of a data packet transmittedbetween two or more computer processes wherein the data packet mayinclude video data. The data packet can include a cookie and/orassociated contextual information, for example. The system 1000 includesa communication framework 1006 (e.g., a global communication networksuch as the Internet, or mobile network(s)) that can be employed tofacilitate communications between the client(s) 1002 and the server(s)1004.

Communications can be facilitated via a wired (including optical fiber)and/or wireless technology. The client(s) 1002 are operatively connectedto one or more client data store(s) 1008 that can be employed to storeinformation local to the client(s) 1002 (e.g., cookie(s) and/orassociated contextual information). Similarly, the server(s) 1004 areoperatively connected to one or more server data store(s) 1010 that canbe employed to store information local to the servers 1004.

In one embodiment, a client 1002 can transfer an encoded file, inaccordance with the disclosed subject matter, to server 1004. Server1004 can store the file, decode the file, or transmit the file toanother client 1002. It is to be appreciated, that a client 1002 canalso transfer uncompressed file to a server 1004 and server 1004 cancompress the file in accordance with the disclosed subject matter.Likewise, server 1004 can encode video information and transmit theinformation via communication framework 1006 to one or more clients1002.

The illustrated aspects of the disclosure may also be practiced indistributed computing environments where certain tasks are performed byremote processing devices that are linked through a communicationsnetwork. In a distributed computing environment, program modules can belocated in both local and remote memory storage devices.

Moreover, it is to be appreciated that various components describedherein can include electrical circuit(s) that can include components andcircuitry elements of suitable value in order to implement theembodiments of the subject innovation(s). Furthermore, it can beappreciated that many of the various components can be implemented onone or more integrated circuit (IC) chips. For example, in oneembodiment, a set of components can be implemented in a single IC chip.In other embodiments, one or more of respective components arefabricated or implemented on separate IC chips.

What has been described above includes examples of the embodiments ofthe present invention. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing the claimed subject matter, but it is to be appreciated thatmany further combinations and permutations of the subject innovation arepossible. Accordingly, the claimed subject matter is intended to embraceall such alterations, modifications, and variations that fall within thespirit and scope of the appended claims. Moreover, the above descriptionof illustrated embodiments of the subject disclosure, including what isdescribed in the Abstract, is not intended to be exhaustive or to limitthe disclosed embodiments to the precise forms disclosed. While specificembodiments and examples are described herein for illustrative purposes,various modifications are possible that are considered within the scopeof such embodiments and examples, as those skilled in the relevant artcan recognize. Moreover, use of the term “an embodiment” or “oneembodiment” throughout is not intended to mean the same embodimentunless specifically described as such.

In particular and in regard to the various functions performed by theabove described components, devices, circuits, systems and the like, theterms used to describe such components are intended to correspond,unless otherwise indicated, to any component which performs thespecified function of the described component (e.g., a functionalequivalent), even though not structurally equivalent to the disclosedstructure, which performs the function in the herein illustratedexemplary aspects of the claimed subject matter. In this regard, it willalso be recognized that the innovation includes a system as well as acomputer-readable storage medium having computer-executable instructionsfor performing the acts and/or events of the various methods of theclaimed subject matter.

The aforementioned systems/circuits/modules have been described withrespect to interaction between several components/blocks. It can beappreciated that such systems/circuits and components/blocks can includethose components or specified sub-components, some of the specifiedcomponents or sub-components, and/or additional components, andaccording to various permutations and combinations of the foregoing.Sub-components can also be implemented as components communicativelycoupled to other components rather than included within parentcomponents (hierarchical). Additionally, it should be noted that one ormore components may be combined into a single component providingaggregate functionality or divided into several separate sub-components,and any one or more middle layers, such as a management layer, may beprovided to communicatively couple to such sub-components in order toprovide integrated functionality. Any components described herein mayalso interact with one or more other components not specificallydescribed herein but known by those of skill in the art.

In addition, while a particular feature of the subject innovation mayhave been disclosed with respect to only one of several implementations,such feature may be combined with one or more other features of theother implementations as may be desired and advantageous for any givenor particular application. Furthermore, to the extent that the terms“includes,” “including,” “has,” “contains,” variants thereof, and othersimilar words are used in either the detailed description or the claims,these terms are intended to be inclusive in a manner similar to the term“comprising” as an open transition word without precluding anyadditional or other elements.

As used in this application, the terms “component,” “module,” “system,”or the like are generally intended to refer to a computer-relatedentity, either hardware (e.g., a circuit), a combination of hardware andsoftware, software, or an entity related to an operational machine withone or more specific functionalities. For example, a component may be,but is not limited to being, a process running on a processor (e.g.,digital signal processor), a processor, an object, an executable, athread of execution, a program, and/or a computer. By way ofillustration, both an application running on a controller and thecontroller can be a component. One or more components may reside withina process and/or thread of execution and a component may be localized onone computer and/or distributed between two or more computers. Further,a “device” can come in the form of specially designed hardware;generalized hardware made specialized by the execution of softwarethereon that enables the hardware to perform specific function; softwarestored on a computer readable medium; or a combination thereof.

Moreover, the words “example” or “exemplary” are used herein to meanserving as an example, instance, or illustration. Any aspect or designdescribed herein as “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs. Rather, use ofthe words “example” or “exemplary” is intended to present concepts in aconcrete fashion. As used in this application, the term “or” is intendedto mean an inclusive “or” rather than an exclusive “or”. That is, unlessspecified otherwise, or clear from context, “X employs A or B” isintended to mean any of the natural inclusive permutations. That is, ifX employs A; X employs B; or X employs both A and B, then “X employs Aor B” is satisfied under any of the foregoing instances. In addition,the articles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform.

Computing devices typically include a variety of media, which caninclude computer-readable storage media and/or communications media, inwhich these two terms are used herein differently from one another asfollows. Computer-readable storage media can be any available storagemedia that can be accessed by the computer, is typically of anon-transitory nature, and can include both volatile and nonvolatilemedia, removable and non-removable media. By way of example, and notlimitation, computer-readable storage media can be implemented inconnection with any method or technology for storage of information suchas computer-readable instructions, program modules, structured data, orunstructured data. Computer-readable storage media can include, but arenot limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disk (DVD) or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or other tangible and/or non-transitorymedia which can be used to store desired information. Computer-readablestorage media can be accessed by one or more local or remote computingdevices, e.g., via access requests, queries or other data retrievalprotocols, for a variety of operations with respect to the informationstored by the medium.

On the other hand, communications media typically embodycomputer-readable instructions, data structures, program modules orother structured or unstructured data in a data signal that can betransitory such as a modulated data signal, e.g., a carrier wave orother transport mechanism, and includes any information delivery ortransport media. The term “modulated data signal” or signals refers to asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in one or more signals. By way ofexample, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

What is claimed is:
 1. A system, comprising: a memory that has storedthereon computer executable components; a processor that executes thefollowing computer executable components stored in the memory: adetection component that detects a change event associated with a mediaitem of a plurality of media items, the change event associated withdata representing a description of the media item, wherein thedescription of the media item is metadata; a policy component that, inresponse to the detection component detecting the change event,determines a set of media items of the plurality of media items that arerelated to the change event, determining the set of media itemscomprising: determining a classification of the media item associatedwith the change event; identifying a plurality of media item clusterscontaining the media item associated with the change event; andselecting a cluster of the plurality of media item clusters responsiveto the determined classification of the media item associated with thechange event, the selected cluster containing a plurality of clusteredmedia items; the policy component further determining a correctiveaction to perform on the plurality of clustered media items based on thechange event; and a merging component that determines whether to alter,in response to the policy component determining the corrective action,data representing the descriptions of the clustered media items.
 2. Thesystem of claim 1, wherein detecting the change event by the detectioncomponent comprise at least one of: detecting a change of the datarepresenting the description of the media item; or detecting aninconsistency between the data representing the description of the mediaitem and the data representing the descriptions of the clustered mediaitems.
 3. The system of claim 2, wherein the policy component determinesa type associated with the change or the inconsistency.
 4. The system ofclaim 3, wherein the merging component alters the data representing thedescriptions of the clustered media items based on the type associatedwith the change or the inconsistency.
 5. The system of claim 3, whereinthe type comprises an exclusive type or non-exclusive type.
 6. Thesystem of claim 1, wherein the policy component determines thecorrective action based on an evaluation of classifications of theclustered media items.
 7. The system of claim 6, wherein the policycomponent determines the corrective action based on a classificationdetermined to apply to a majority of the clustered media items.
 8. Thesystem of claim 1, wherein the merging component alters the datarepresenting the descriptions of the clustered media items based onassociated confidence scores of the respective data.
 9. The system ofclaim 1, further comprising: a reporting component that, in response tothe policy component determining the corrective action, generates areport that indicates at least one of the determined corrective actionor the change event.
 10. A method comprising: employing a processor toexecute computer executable instructions stored in a memory to performthe following acts: detecting a change event associated with a mediaitem of a plurality of media items, the change event associated withdata representing a description of the media item, wherein thedescription of the media item is metadata; in response to detecting thechange event, determining a set of media items of the plurality of mediaitems that are related to the change event, determining the set of mediaitems comprising: determining a classification of the media itemassociated with the change event; identifying a plurality of media itemclusters containing the media item associated with the change event; andselecting a cluster of the plurality of media item clusters responsiveto the determined classification of the media item associated with thechange event, the selected cluster containing a plurality of clusteredmedia items; and determining, in response to detecting the change event,a corrective action to perform on the plurality of clustered media itemsbased on the change event.
 11. The method of claim 10, the acts furthercomprising: determining whether to alter, in response to determining thecorrective action, data representing the descriptions of the clusteredmedia items.
 12. The method of claim 10, the acts further comprising: inresponse to determining the corrective action, generating a reportcomprising data describing the corrective action.
 13. The method ofclaim 10, the acts further comprising: receiving input regarding thecorrective action; and determining a new corrective action based on theinput.
 14. A computer readable storage device comprising instructionsthat, in response to execution, cause a system comprising a processor toperform operations, comprising: detecting a change event associated witha media item of a plurality of media items, the change event associatedwith data representing a description of the media item, wherein thedescription of the media item is metadata; in response to detecting thechange event, determining a set of media items of the plurality of mediaitems that are related to the change event, determining the set of mediaitems comprising: determining a classification of the media itemassociated with the change event; identifying a plurality of media itemclusters containing the media item associated with the change event; andselecting a cluster of the plurality of media item clusters responsiveto the determined classification of the media item associated with thechange event, the selected cluster containing a plurality of clusteredmedia items; and determining, in response to detecting the change event,a corrective action to perform on the plurality of clustered media itemsbased on the change event.
 15. The computer readable storage device ofclaim 14, wherein detecting the change event comprises: determiningwhether the data representing a description of the media item isaltered; or determining whether the data representing a description ofthe media item is inconsistent with data representing a description of asecond media item.
 16. The computer readable storage device of claim 14wherein the operations further comprise: determining the correctiveaction based on an alteration policy.
 17. The computer readable storagedevice of claim 16, wherein the operations further comprise: determiningthe alteration policy based on a history of at least one of correctiveactions or user input.